Search Results (10,797)

Enhanced oil recovery (EOR) methods are generally employed in depleted reservoirs to increase the recovery factor beyond that of water flooding. Polymer flooding is one of the major EOR methods. EOR polymer solutions (especially the synthetic ones characterized by flexible chains) that flow through porous media are not only subjected to shearing forces but also extensional deformation, and therefore, they exhibit not only Newtonian and shear thinning behavior but also shear thickening behavior at a certain porous media shear rate/velocity. Shear rheometry has been widely used to characterize the rheological properties of EOR polymer systems. This paper aims to investigate the effect of the polymers’ concentrations, ranging from 25 ppm to 2500 ppm, on the viscous, linear, and non-linear viscoelastic properties of hydrolyzed polyacrylamide (HPAM) in shear field and porous media. The results observed indicate that viscous properties such as Newtonian viscosity increase monotonically with the increase in concentration in both fields. However, linear viscoelastic properties, such as shear characteristic time, were absent for concentrations not critical in both shear rheometry and porous media. Beyond the critical association concentration (CAC), the modified shear thinning index decreases in terms of concentration in both fields, signifying their intensified thinning. At those concentrations higher than CAC, the viscoelastic onset rate remains constant in both fields. In both fields, the shear thickening index, a strict non-linear viscoelastic property, initially increases with concentration and then decreases with concentration, signifying that the polymer chains do not stretch significantly at higher concentrations. Also, another general observation is that the rheological properties of the polymer solutions in both porous media and shear rheometry only follow a similar trend if the concentration is higher than the CAC. At concentrations less than the CAC, the shear and porous media onset rates follow different trends, possibly due to the higher inertial effect in the rheometer. Full article

Urbanization is defining global change, and megacities are fast becoming a hallmark of the Anthropocene. Humanity’s pursuit toward sustainability is reliant on the successful management of these massive urban centers and their progression into sustainable and resilient settlements. Indicators and indices are applied assessment and surveillance tools used to measure, monitor, and gauge the sustainable development and urban resilience of megacities. Unknown is how indicator-based evaluations of sustainable development and urban resilience of the world’s largest 43 cities compare. In response, this review paper used the PRISMA reporting protocol, governed by 33 established and 10 emerging megacities, to compare and contrast evaluations of sustainable development and urban resilience. Results reveal that applied assessments of sustainable development of megacities appeared earlier in time and were more abundant than those of urban resilience. Geographically, China dominated other nations in affiliations to scientific research for both sustainable development and urban resilience of megacities. Among the 100 most recurrent terms, three distinct key term clusters formed for sustainable development; seven budding key term clusters formed for urban resilience suggesting breadth in contrast to sustainable development depth. The most cited assessments of sustainable development emphasize topics of energy, methodological approaches, and statistical modeling. The most cited assessments of urban resilience emphasize topics of flooding, transit networks, and disaster risk resilience. Megacities research is dominated by few countries, suggesting a need for inclusion and international partnerships. Lastly, as the world’s people become increasingly urbanized, sustainable development and urban resilience of megacities will serve as a key barometer for humanity’s progress toward sustainability. Full article

In the arid Arabian Peninsula, particularly within the United Arab Emirates (UAE), the perception of rainfall has shifted from a natural blessing to a significant challenge for infrastructure and community resilience. The unprecedented storm on 17 April 2024, exposed critical vulnerabilities in the UAE’s urban infrastructure and flood management practices, revealing substantial gaps in handling accumulated precipitation. This study addresses the necessity of updating the Intensity–Duration–Frequency (IDF) curves for the Sharjah Emirate by utilizing recent precipitation data from 2021 to April 2024, alongside previously published 2020 data. By recalibrating the IDF curves based on data from three meteorological stations, this study reveals a substantial increase in rainfall intensities across all durations and return periods. Rainfall intensities increased by an average of 36.76% in Sharjah, 26.52% in Al Dhaid, and 17.55% in Mleiha. These increases indicate a trend towards more severe and frequent rainfall events, emphasizing the urgent need to revise hydrological models and infrastructure designs to enhance flood resilience. This study contributes valuable insights for policymakers, urban planners, and disaster management authorities in the UAE and similar regions worldwide. Full article

Water extraction from synthetic aperture radar (SAR) images has an important application value in wetland monitoring, flood monitoring, etc. However, it still faces the problems of low generalization, weak extraction ability of detailed information, and weak suppression of background noises. Therefore, a new framework, Multi-scale Attention Detailed Feature fusion Network (MADF-Net), is proposed in this paper. It comprises an encoder and a decoder. In the encoder, ResNet101 is used as a solid backbone network to capture four feature levels at different depths, and then the proposed Deep Pyramid Pool (DAPP) module is used to perform multi-scale pooling operations, which ensure that key water features can be captured even in complex backgrounds. In the decoder, a Channel Spatial Attention Module (CSAM) is proposed, which focuses on feature areas that are critical for the identification of water edges by fusing attention weights in channel and spatial dimensions. Finally, the high-level semantic information is effectively fused with the low-level edge features to achieve the final water detection results. In the experiment, Sentinel-1 SAR images of three scenes with different characteristics and scales of water body are used. The PA and IoU of water extraction by MADF-Net can reach 92.77% and 89.03%, respectively, which obviously outperform several other networks. MADF-Net carries out water extraction with high precision from SAR images with different backgrounds, which could also be used for the segmentation and classification of other tasks from SAR images. Full article

The urban areas of Mediterranean Europe, and particularly Italy, have experienced considerable expansion since the late 19th century in terms of settlements, structures, and infrastructure, especially in large population centers. In such areas, the geohydrological risk is high not only for inhabited areas but also along roadways exposed to flooding. This scenario is worrying, especially in road underpass sections, where drivers are unlikely to perceive a real risk due to the high degree of confidence that comes from the habit of driving. Underpasses have been widely used to obviate the need to find shorter alternative routes and manage vehicular traffic in urban settings impeded by previous anthropogenic and natural constraints. To assess the numerical consistency, frequency, and areal distribution of flood risk around road underpasses, several hundred pieces of data were selected (mostly from international, national and local newspapers, CNR IRPI archive and local archives) and cataloged in a thematic database, referring mainly to the Italian territory. The behavioral aspects in the face of risk were also examined in order to provide a better understanding and raise awareness for preventive purposes. The results of this specific CNR research, which lasted about two years, confirm the exposure of underpasses to extreme risk events, affecting road users. In Italy alone, between 1942 and 2023, 698 underpasses were identified as having experienced a flooding event at least once. The database shows that 680 vehicles were involved in Italy, with a total of at least 812 individuals, of whom 19 died. Despite incomplete and uneven information, the findings of the analysis regarding the increment in underpasses flooding and the drivers action in front of a flooded underpass may be useful for undertaking the appropriate mitigation strategies. Full article

Computational models for water resources often experience slow execution times, limiting their application. Metamodels, especially those based on machine learning, offer a promising alternative. Our research extends a prior Graph Neural Network (GNN) metamodel for the Storm Water Management Model (SWMM), which efficiently learns with less data and generalizes to new UDS sections via transfer learning. We extend the metamodel’s functioning by adding flowrate prediction, crucial for assessing water quality and flooding risks. Using an Encoder–Processor–Decoder architecture, the metamodel displays high accuracy on the simulated time series. Future work is aimed at incorporating more physical principles and testing further transferability. Full article

Inundation maps that show water depths that occur in the event of a flood are essential for protection. Especially information on timings is crucial. Creating a dynamic inundation map with depth data in temporal resolution is a major challenge and is not possible with physical models, as these are too slow for real-time predictions. To provide a dynamic inundation map in real-time, we developed a data-driven multi-step inundation forecast system for fluvial flood events. The forecast system is based on a convolutional neural network (CNN), feature-informed dense layers, and a recursive connection from the predicted inundation at timestep t as a new input for timestep t + 1. The forecast system takes a hydrograph as input, cuts it at desired timesteps (t), and outputs the respective inundation for each timestep, concluding in a dynamic inundation map with a temporal resolution (t). The prediction shows a Critical Success Index (CSI) of over 90%, an average Root Mean Square Error (RMSE) of 0.07, 0.12, and 0.15 for the next 6 h, 12 h, and 24 h, respectively, and an individual RMSE value below 0.3 m, for all test datasets when compared with the results from a physically based model. Full article

Several cities are facing an increasing flood risk due to the coupled effect of climate change and urbanization. Non-structural protection strategies, such as Early Warning Systems (EWSs), have demonstrated significant potential in mitigating hydraulic risk and often become the primary option when the implementation of structural measures is impeded by the complexities of urban environments. This study presents a new EWS designed specifically for fluvial floods in the city of Palermo (Italy), which is crossed by the Oreto River. The system is based on the preliminary definition of various Flood Event Scenarios (FESs) as a function of typical precursors, such as rainfall forecasts, and antecedent wetness and river flow conditions. Antecedent conditions are derived from real-time water stage observations at an upstream river section, while rainfall forecasts are provided by the Italian National Surveillance Meteorological Bulletins with a preannouncement time of up to 36 h. An innovative feature of the system is the use of rainfall Depth–Duration Thresholds to predict the expected hydrograph peak, significantly reducing warning issuing times. A specific FES, immediately accessible from a pre-built library, can be linked to any combination of precursors. Each FES predicts the timing and location of the first points of flooding; flood-prone areas and water depths; and specific hazard maps for elements typically exposed in cities, such as people, vehicles, and buildings. The EWS has been tested on a historical flood event, demonstrating satisfactory accuracy in reproducing the location, extent, and severity of the flood. Full article

Our target USH reservoir in the D oilfield is characterized by “inverse rhythm” deposition with the noticeable features of “high porosity and low permeability”. The reservoir has been developed with waterflooding using horizontal wells. Due to the strong heterogeneity of the reservoir, water channeling is severe, and the water cut has reached 79%. Considering the high temperature and high salinity reservoir conditions, polymer microspheres (PMs) were selected to realize conformance control. In this study, characterization of the polymer microsphere suspension was achieved via morphology, size distribution, and viscosity measurement. Furthermore, a multi-scale visualization study of the reservoir development process, including waterflooding, polymer microsphere flooding, and subsequent waterflooding, was conducted using macro-scale coreflooding and calcite-etched micromodels. It was revealed that the polymer microspheres could swell in the high salinity brine (170,000 ppm) by 2.7 times if aged for 7 days, accompanied by a viscosity increase. This feature is beneficial for the injection at the wellbore while swelled to work as a profile control agent in the deep formation. The macro-scale coreflood with a 30 cm × 30 cm × 4.5 cm layer model with 108 electrodes installed enabled the oil distribution visualization from different perpendicular cross sections. In this way, the in situ conformance control ability of the polymer microsphere was revealed both qualitatively and quantitatively. Furthermore, building on the calcite-etched visible micro-model, the pore-scale variation of the residual oil when subjected to waterflooding, polymer microsphere waterflooding, and subsequent waterflooding was collected, which revealed the oil displacement efficiency increase by polymer microspheres directly. The pilot test in the field also proves the feasibility of conformance control by the polymer microspheres, i.e., more than 40,000 bbls of oil increase was observed in the produces, accompanied by an obvious water reduction. Full article

The hydrologic performance and cost-effectiveness of green stormwater infrastructure (GSI) in climates with highly variable precipitation is an important subject in urban stormwater management. We measured the hydrologic effects of two bioretention basins in San Antonio, Texas, a growing city in a region prone to flash flooding. Pre-construction, inflow, and outflow hydrographs of the basins were compared to test whether the basins reduced peak flow magnitude and altered the metrics of flashiness, including rate of flow rise and fall. We determined the construction and annual maintenance cost of one basin and whether precipitation magnitude and antecedent moisture conditions altered hydrologic mitigation effectiveness. The basins reduced flashiness when comparing inflow to outflow and pre-construction to outflow hydrographs, including reducing peak flow magnitudes by >80% on average. Basin performance was not strongly affected by precipitation magnitude or antecedent conditions, though the range of precipitation magnitudes sampled was limited. Construction costs were higher than previously reported projects, but annual maintenance costs were similar and no higher than costs to maintain an equivalent landscaped area. Results indicate that bioretention basins effectively mitigate peak flow and flashiness, even in flash-flood-prone environments, which should benefit downstream ecosystems. The results provide a unique assessment of bioretention basin performance in flash-flood-prone environments and can inform the optimization of cost-effectiveness when implementing GSI at watershed scales in regions with current or future similar precipitation regimes. Full article

Evaluating flood risk though numerical simulations in areas where hydrometric and bathymetric data are scarcely available is a challenge. This is, however, of paramount importance, particularly in urban areas, where huge losses of human life and extensive damage can occur. This paper focuses on the 2–3 June 2023 event at Léogâne in Haiti, where the Rouyonne River partly flooded the city. Water depths in the river have been recorded since April 2022, and a few discharges were measured manually, but these were not sufficient to produce a reliable rating curve. Using a uniform-flow assumption combined with the Bayesian rating curve (BaRatin) method, it was possible to extrapolate the existing data to higher discharges. From there, a rainfall–runoff relation was developed for the site using a distributed hydrological model, which allowed the discharge of the June 2023 event to be determined, which was estimated as twice the maximum conveying capacity of the river in the measurement section. Bathymetric data were obtained using drone-based photogrammetry, and two-dimensional simulations were carried out to represent the flooded area and the associated water depths. By comparing the water depths of 21 measured high-water marks with the simulation results, we obtained a Kling–Gupta Efficiency (KGE) and Nash–Sutcliffe Efficiency (NSE) values of 0.890 and 0.882, respectively. This allows us to conclude that even when only scarce official data are available, it is possible to use field data acquired by low-cost methodologies to build a model that is sufficiently accurate and that can be used by flood managers and decision makers to assess flood risk and vulnerability in Haiti. Full article

Sahelian countries including Burkina Faso face multiple challenges related to climatic conditions. Setting up effective disaster management plans is essential for protecting livelihoods and promoting sustainable development. Soil and water conservation measures (SWCMs) are emerging as key components of such plans, particularly in Burkina Faso. However, there is an insufficiency of studies exploring their potential as green infrastructures in the Sahelian context and this research aims to contribute to filling this gap. We used national data, remote sensing, and GIS tools to assess SWCM adoption and the potential for climate resilience. Stone ribbons emerged as the most widely adopted SWCM, covering 2322.4 km² especially in the northern regions, while filtering dikes were the least widely adopted, at 126.4 km². Twenty years of NDVI analysis showed a notable vegetation increase in Yatenga (0.075), Oudalan (0.073), and provinces with a high prevalence of SWCM practices. There was also an apparent increase in SWCM percentages from 60% of land degradation. Stone ribbons could have led to a runoff reduction of 13.4% in Bam province, highlighting their effectiveness in climate resilience and flood risk mitigation. Overall, encouraging the adoption of SWCMs offers a sustainable approach to mitigating climate-related hazards and promoting resilience in Sahelian countries such as Burkina Faso. Full article

Floods often cause significant damage to transportation infrastructure such as roads, railways, and bridges. This study identifies several topographic, environmental, and hydrological factors (slope, elevation, rainfall, land use and cover, distance from rivers, geology, topographic wetness index, and drainage density) influencing the safety of the railway infrastructure and uses multi-criteria analysis (MCA) alongside an analytical hierarchy process (AHP) to produce flood susceptibility maps within a geographic information system (GIS). The proposed methodology was applied to the catchment area of a railway track in southern Italy that was heavily affected by a destructive flood that occurred in the autumn of 2015. Two susceptibility maps were obtained, one based on static geophysical factors and another including triggering rainfall (dynamic). The results showed that large portions of the railway line are in a very highly susceptible zone. The flood susceptibility maps were found to be in good agreement with the post-disaster flood-induced infrastructural damage recorded along the railway, whilst the official inundation maps from competent authorities fail to supply information about flooding occurring along secondary tributaries and from direct rainfall. The reliable identification of sites susceptible to floods and damage may provide railway and environmental authorities with useful information for preparing disaster management action plans, risk analysis, and targeted infrastructure maintenance/monitoring programs, improving the resilience capacity of the railway network. The proposed approach may offer railway authorities a cost-effective strategy for rapidly screening flood susceptibility at regional/national levels and could also be applied to other types of linear transport infrastructures. Full article

Hydrodynamic models are often used to obtain insights into potential dike breaches, because dike breaches can have severe consequences. However, their high computational cost makes them unsuitable for real-time flood forecasting. Machine learning models are a promising alternative, as they offer reasonable accuracy at a significant reduction in computation time. In this study, we explore the effectiveness of a Long Short-Term Memory (LSTM) neural network in fast flood modelling for a dike breach in the Netherlands, using training data from a 1D–2D hydrodynamic model. The LSTM uses the outflow hydrograph of the dike breach as input and produces water depths on all grid cells in the hinterland for all time steps as output. The results show that the LSTM accurately reflects the behaviour of overland flow: from fast rising and high water depths near the breach to slowly rising and lower water depths further away. The water depth prediction is very accurate (MAE = 0.045 m, RMSE = 0.13 m), and the inundation extent closely matches that of the hydrodynamic model throughout the flood event (Critical Success Index = 94%). We conclude that machine learning techniques are suitable for fast modelling of the complex dynamics of dike breach floods. Full article

Over the past few decades, communication has evolved significantly, driven by new technologies and digital connections, with the Internet and mobile phones transforming traditional communication methods. This shift has also impacted disaster risk awareness-raising, requiring messages to adapt to modern digital platforms. This article describes an effort to engage younger generations with the issue of sea level rise, critical yet often overlooked despite its significant impact on global coastal areas, through the serious digital game “Inundation”. Presented for the first time, the game offers an engaging experience where players protect territories from coastal flooding while understanding rising seas’ causes, effects, and impacts. Feedback from student beta testers highlighted the game’s effectiveness in conveying scientific concepts and increasing awareness about this pressing issue. The game’s innovative design, particularly its visual representation of sea level rise at a pace more relatable to human perception, fills a gap in environmental education by making complex topics accessible and engaging. While evaluating the impact of such tools is challenging, initial feedback suggests that “Inundation” has significant potential to foster disaster preparedness and proactive safeguarding actions. Full article